Deep reinforcement learning for inventory control: A roadmap

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

“…However, it must be assumed that the complex structure of current SCs, especially global ones with many stages and nodes, the number of variables included in the modeled problem and its intrinsically stochastic condition imply that the modeling of real cases with the reinforcement learning methodology, but without the additional assistance of other methods, constitutes a considerable challenge. Only through the gradual incorporation of the DRL methodology [69], a combination of the reinforcement learning methodology with deep learning-another ML methodology that uses artificial neural networks to transform a set of inputs into a set of outputs, that solve tasks that involve handling complex and high-dimensional raw input data sets [91]-has it been possible to begin to consider the study of SCs with certain complexity, e.g.,: (i) the multistage SC problem of Alves and Mateus [67], validated with a four-stage SC scenario and two nodes per stage, local inventories, lead time, a single product, and demand uncertainty; (ii) the capacitated SC problem of Peng et al [68], validated with a three-stage SC scenario, one node in the first, two in the second and three in the last stage, capacitated production, independent, stochastic and seasonal demand, and a single product; (iii) the case of Meisheri et al [92] who, despite restricting the validation of their retailers' inventory replenishment to the last SC layers, i.e., warehouse and retailer, considers the existence of product variety, with instances of 100 and 220 products-to substantially increase combinatorial computation-and incorporates lead time, limited storage capacity, cross-product restrictions, and weight and volume transportation restrictions. Computational limitations in this regard are manifested as the size of the problem to be solved in terms of the size of the input dataset, and especially the size of the modeled problem's observation space.…”

“…In it the adequate and stable supply of raw materials is assumed, but the plant's production capacity is limited. The article of Boute et al [69] offers a conceptual approach, and its objective is to describe the key design choices of DRL algorithms to facilitate their implementation into the inventory control task in SCs. It first introduces MDPs for inventory control optimization in their different solution approaches.…”

“…Both processes are based on the DRL method [69], and are basically developed by two elements, the training environment and the DRL agent (Figure 4), to be implemented into a DRL framework based on the Python code with the help of its specialized open source libraries.…”

“…Both processes are based on the DRL method [69], and are basically developed by two elements, the training environment and the DRL agent (Figure 4), to be implemented into a DRL framework based on the Python code with the help of its specialized open source libraries. The training environment is the MPS modeled as an MDP in such a way that it is made up of: (i) an observation space; (ii) an action space; (iii) an initial state; (iv) the state transition function.…”

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

“…Potential applications of machine learning in the e-commerce sector have been researched extensively from different perspectives (e.g., chatbots [11], recommendation engines [12][13][14], applications for intelligent logistics [15,16] and pricing [17][18][19][20][21]). The application of machine learning, as in other sales business models, extends to almost every area of e-commerce (e.g., security [22], fraud detection [23,24], profit maximisation [25], sales prediction [26,27], inventory management [28,29], product categorisation [30], and portfolio management [31]). Literature reviews exploring machine learning applications in different e-commerce scenarios can mainly be found in [32][33][34][35][36][37][38].…”